Abstract: Systems for intelligent sorting of time series data for improved contextual messaging are included herein. An intelligent sorting server may receive time series data comprising a plurality of chat messages. The intelligent sorting server may determine a first order of the plurality of chat messages based on a chronologic order. The intelligent sorting server may use one or more machine learning classifiers to identify candidates for reordering the chat messages. The intelligent sorting server may generate a second order of the chat messages based on the identified candidates for reordering. Accordingly, the intelligent sorting server may present, to a client device, a transcript of the chat messages associated with the second order and an indication that at least one chat message has been repositioned.

Abstract: Systems for intelligent sorting of time series data for improved contextual messaging are included herein. An intelligent sorting server may receive time series data comprising a plurality of chat messages. The intelligent sorting server may determine a first order of the plurality of chat messages based on a chronologic order. The intelligent sorting server may use one or more machine learning classifiers to identify candidates for reordering the chat messages. The intelligent sorting server may generate a second order of the chat messages based on the identified candidates for reordering. Accordingly, the intelligent sorting server may present, to a client device, a transcript of the chat messages associated with the second order and an indication that at least one chat message has been repositioned.

Abstract: Disclosed embodiments may include a method for prioritizing messages. The system may include receiving one or more messages comprising message data and application sender data. From the message data and application sender data, the system may determine a ranking of importance of the one or more messages, and then determine whether a first message is urgent. If the first message is urgent, the system may send the first message to the user device. If the first message is not urgent, the system may determine a set time for the first message to be sent and send the first message to the user device at the set time.

Abstract: Aspects discussed herein relate to the storage of data in graph databases and detecting fraudulent behavior in the stored data. Fraud detection systems may use graph databases to store data, allowing for querying the graph database to obtain data using a variety of graph semantics such as nodes, edges, and properties. Graph databases in accordance with embodiments of the invention may include account nodes and attribute nodes, where nodes of the same type are not directly linked to each other. When a particular node is updated, an updated node may be created with a higher version number than the existing node. Each node may include an indication of the node being associated with fraudulent activity. Fraud indicators may be calculated based on the relationships between the nodes and fraud indicators for the nodes.

Abstract: Systems for intelligent sorting of time series data for improved contextual messaging are included herein. An intelligent sorting server may receive time series data comprising a plurality of chat messages. The intelligent sorting server may determine a first order of the plurality of chat messages based on a chronologic order. The intelligent sorting server may use one or more machine learning classifiers to identify candidates for reordering the chat messages. The intelligent sorting server may generate a second order of the chat messages based on the identified candidates for reordering. Accordingly, the intelligent sorting server may present, to a client device, a transcript of the chat messages associated with the second order and an indication that at least one chat message has been repositioned.

Abstract: Systems for intelligent sorting of time series data for improved contextual messaging are included herein. An intelligent sorting server may receive time series data comprising a plurality of chat messages. The intelligent sorting server may determine a first order of the plurality of chat messages based on a chronologic order. The intelligent sorting server may use one or more machine learning classifiers to identify candidates for reordering the chat messages. The intelligent sorting server may generate a second order of the chat messages based on the identified candidates for reordering. Accordingly, the intelligent sorting server may present, to a client device, a transcript of the chat messages associated with the second order and an indication that at least one chat message has been repositioned.

Abstract: Systems for intelligent sorting of time series data for improved contextual messaging are included herein. An intelligent sorting server may receive time series data comprising a plurality of chat messages. The intelligent sorting server may determine a first order of the plurality of chat messages based on a chronologic order. The intelligent sorting server may use one or more machine learning classifiers to identify candidates for reordering the chat messages. The intelligent sorting server may generate a second order of the chat messages based on the identified candidates for reordering. Accordingly, the intelligent sorting server may present, to a client device, a transcript of the chat messages associated with the second order and an indication that at least one chat message has been repositioned.

Abstract: Aspects discussed herein relate to the storage of data in graph databases and detecting fraudulent behavior in the stored data. Fraud detection systems may use graph databases to store data, allowing for querying the graph database to obtain data using a variety of graph semantics such as nodes, edges, and properties. Graph databases in accordance with embodiments of the invention may include account nodes and attribute nodes, where nodes of the same type are not directly linked to each other. When a particular node is updated, an updated node may be created with a higher version number than the existing node. Each node may include an indication of the node being associated with fraudulent activity. Fraud indicators may be calculated based on the relationships between the nodes and fraud indicators for the nodes.

Abstract: Aspects discussed herein relate to the storage of data in graph databases and detecting fraudulent behavior in the stored data. Fraud detection systems may use graph databases to store data, allowing for querying the graph database to obtain data using a variety of graph semantics such as nodes, edges, and properties. Graph databases in accordance with embodiments of the invention may include account nodes and attribute nodes, where nodes of the same type are not directly linked to each other. When a particular node is updated, an updated node may be created with a higher version number than the existing node. Each node may include an indication of the node being associated with fraudulent activity. Fraud indicators may be calculated based on the relationships between the nodes and fraud indicators for the nodes.

Abstract: Aspects discussed herein relate to the storage of data in graph databases and detecting fraudulent behavior in the stored data. Fraud detection systems may use graph databases to store data, allowing for querying the graph database to obtain data using a variety of graph semantics such as nodes, edges, and properties. Graph databases in accordance with embodiments of the invention may include account nodes and attribute nodes, where nodes of the same type are not directly linked to each other. When a particular node is updated, an updated node may be created with a higher version number than the existing node. Each node may include an indication of the node being associated with fraudulent activity. Fraud indicators may be calculated based on the relationships between the nodes and fraud indicators for the nodes.

Abstract: Aspects discussed herein relate to the storage of data in graph databases and detecting fraudulent behavior in the stored data. Fraud detection systems may use graph databases to store data, allowing for querying the graph database to obtain data using a variety of graph semantics such as nodes, edges, and properties. Graph databases in accordance with embodiments of the invention may include account nodes and attribute nodes, where nodes of the same type are not directly linked to each other. When a particular node is updated, an updated node may be created with a higher version number than the existing node. Each node may include an indication of the node being associated with fraudulent activity. Fraud indicators may be calculated based on the relationships between the nodes and fraud indicators for the nodes.